Ubiquitin: Molecular modeling and simulations

The synthesis and destruction of proteins are imperative for maintaining their cellular homeostasis. In the 1970s, Aaron Ciechanover, Avram Hershko, and Irwin Rose discovered that certain proteins are tagged by ubiquitin before degradation, a discovery that awarded them the 2004 Nobel Prize in Chemistry. Compelling data gathered during the last several decades show that ubiquitin plays a vital role not only in protein degradation but also in many cellular functions including DNA repair processes, cell cycle regulation, cell growth, immune system functionality, hormone-mediated signaling in plants, vesicular trafficking pathways, regulation of histone modification and viral budding. Due to the involvement of ubiquitin in such a large number of diverse cellular processes, flaws and impairments in the ubiquitin system were found to be linked to cancer, neurodegenerative diseases, genetic disorders, and immunological disorders. Hence, deciphering the dynamics and complexity of the ubiquitin system is of significant importance. In addition to experimental techniques, computational methodologies have been gaining increasing influence in protein research and are used to uncover the structure, stability, folding, mechanism of action and interactions of proteins. Notably, molecular modeling and molecular dynamics simulations have become powerful tools that bridge the gap between structure and function while providing dynamic insights and illustrating essential mechanistic characteristics. In this study, we present an overview of molecular modeling and simulations of ubiquitin and the ubiquitin system, evaluate the status of the field, and offer our perspective on future progress in this area of research.     

Agro-ecosystem modeling can aid in the optimization of biomass feedstock supply

Recent European Directives promoted the development of biofuels, requesting mandatory limits to their emissions ot greenhouse gases (GHG). Second-generation biofuels based on lignocellulosic biomass are prime candidates but their GHG emissions are variable and uncertain. Agro-ecosystem modeling can capture them and the performance of biofuel feedstocks.This study aimed at optimizing feedstock supply for a bioethanol unit in France, from agricultural residues, annual and perennial crops. Their productivity and environmental impacts were modelled on a regional scale using geo-referenced data on soil properties, crop management, land-use and future weather data. Several supply scenarios were tested. Cereal straw was the most efficient feedstock but had a low availability, and only miscanthus could meet the bioethanol plant's demand. Sorghum combined poor yields and high GHG emissions compared by miscanthus and triticale. A mix of three biomass sources used less than 3% of the regional agricultural land while abating GHG emissions by 60%.     

A real time experimental setup for classification of epilepsy risk levels

The objective of this paper is to analyze the performance of singular value decomposition, expectation maximization, and Elman Neural Networks in optimization of code converter outputs in the classification of epilepsy risk levels from EEG (electroencephalogram) signals. The signal parameters such as the total number of positive and negative peaks, spikes and sharp waves, their duration etc., were extracted using morphological operators and wavelet transforms. Code converters were considered as a level one classifier. Code converters were found to have a performance index and quality value of 33.26 and 12.74, respectively, which is low. Consequently, for the EEG signals of 20 patients, the post classifiers were applied across 3 epochs of 16 channels. After having made a comparative study of different architectures, SVD was found to be the best post classifier as it marked a performance index of 89.48 and a quality value of 20.62. Elman neural network also exhibits good performance metrics than SVD in the morphological operator based feature extraction method.     

Narrowband signal detection techniques in shallow ocean by acoustic vector sensor array

This paper presents the formulation and performance analysis of four techniques for detection of a narrowband acoustic source in a shallow range-independent ocean using an acoustic vector sensor (AVS) array. The array signal vector is not known due to the unknown location of the source. Hence all detectors are based on a generalized likelihood ratio test (GLRT) which involves estimation of the array signal vector. One non-parametric and three parametric (model-based) signal estimators are presented. It is shown that there is a strong correlation between the detector performance and the mean-square signal estimation error. Theoretical expressions for probability of false alarm and probability of detection are derived for all the detectors, and the theoretical predictions are compared with simulation results. It is shown that the detection performance of an AVS array with a certain number of sensors is equal to or slightly better than that of a conventional acoustic pressure sensor array with thrice as many sensors.     

An extended systematic literature review on provision of evidence for safety certification

ContextCritical systems in domains such as aviation, railway, and automotive are often subject to a formal process of safety certification. The goal of this process is to ensure that these systems will operate safely without posing undue risks to the user, the public, or the environment. Safety is typically ensured via complying with safety standards. Demonstrating compliance to these standards involves providing evidence to show that the safety criteria of the standards are met.ObjectiveIn order to cope with the complexity of large critical systems and subsequently the plethora of evidence information required for achieving compliance, safety professionals need in-depth knowledge to assist them in classifying different types of evidence, and in structuring and assessing the evidence. This paper is a step towards developing such a body of knowledge that is derived from a large-scale empirically rigorous literature review.MethodWe use a Systematic Literature Review (SLR) as the basis for our work. The SLR builds on 218 peer-reviewed studies, selected through a multi-stage process, from 4963 studies published between 1990 and 2012.ResultsWe develop a taxonomy that classifies the information and artefacts considered as evidence for safety. We review the existing techniques for safety evidence structuring and assessment, and further study the relevant challenges that have been the target of investigation in the academic literature. We analyse commonalities in the results among different application domains and discuss implications of the results for both research and practice.ConclusionThe paper is, to our knowledge, the largest existing study on the topic of safety evidence. The results are particularly relevant to practitioners seeking a better grasp on evidence requirements as well as to researchers in the area of system safety. As a major finding of the review, the results strongly suggest the need for more practitioner-oriented and industry-driven empirical studies in the area of safety certification.     

Electric vehicles charging control in a smart grid: A model predictive control approach

The paper presents an event driven model predictive control (MPC) framework for managing charging operations of electric vehicles (EV) in a smart grid. The objective is to minimize the cost of energy consumption, while respecting EV drivers' preferences, technical bounds on the control action (in compliance with the IEC 61851 standard) and both market and grid constraints (by seeking the tracking of a reference load profile defined by the grid operator). The proposed control approach allows “flexible” EV users to participate in demand side management (DSM) programs, which will play a crucial role in improving stability and efficiency of future smart grids. Further, the natural MPC formulation of the problem can be recast into a mixed integer linear programming problem, suitable for implementation on a calculator. Simulation results are provided and discussed in detail.     

Activity theory as a framework for building adaptive e-learning systems: A case to provide empirical evidence

We apply activity theory (AT) to design adaptive e-learning systems (AeLS). AT is a framework to study human’s behavior at learning; whereas, AeLS enhance students’ apprenticeship by the personalization of teaching–learning experiences. AeLS depict users’ traits and predicts learning outcomes. The approach was successfully tested: Experimental group took lectures chosen by the anticipation AT principle; whilst, control group received randomly selected lectures. Learning achieved by experimental group reveals a correlation quite significant and high positive; but, for control group the correlation it is not significant and medium positive. We conclude: AT is a useful framework to design AeLS and provide student-centered education.     

A new method for trust and reputation evaluation in the cloud environments using the recommendations of opinion leaders' entities and removing the effect of troll entities

Trust in the cloud environment is not written into an agreement and is something earned. In any trust evaluation mechanism, opinion leaders are the entities influencing the behaviors or attitudes of others, this makes them to be trustworthy, valid among other characteristics. On the other hand, trolls are the entities posting incorrect and unreal comments; therefore, their effect must be removed. This paper evaluates the trust by considering the influence of opinion leaders on other entities and removing the troll entities’ effect in the cloud environment. Trust value is evaluated using five parameters; availability, reliability, data integrity, identity and capability. Also, we propose a method for opinion leaders and troll entity identification using three topological metrics, including input-degree, output-degree and reputation measures. The method being evaluated in various situation where shows the results of accuracy by removing the effect of troll entities and the advice of opinion leaders.     

An integrated flood management system based on linking environmental models and disaster-related data

The flood disaster management system (FDMS) is a platform developed to provide ongoing disaster reduction capabilities that cover the entire process of flood management. The ontology-based approach links environmental models with disaster-related data to support FDMS-constructed workflows with suitable models and recommend appropriate datasets as model input automatically. This automated activity for model selection and data binding reduces the time-consuming and unreliable operations involved in traditional management techniques, which rely on manual retrieval through simple metadata indices—typically when flood management personnel are overwhelmed with large quantities of observed data. The OpenMI-based modular design used in the system unifies interfaces and data exchange to provide flexible and extensible architecture. Subsequent 3D visualization improves the interpretability of disaster data and the effectiveness of decision-making processes. This paper presents an overview of the design and capabilities of FDMS that provides one-stop management for flood disasters.     

Effect of handle design on movement dynamics and muscle co-activation in a wrist flexion task

Arm and wrist manipulanda are commonly used as input devices in teleoperation and gaming applications, establish a physical interface to patients in several rehabilitation robots, and are applied as advanced research tools in biomechanics and neuroscience. Despite the fact that the physical interface, i.e. the handle through which the wrist/hand is attached to the manipulator, may influence interaction and movement behavior, the effects of handle design on these parameters has received little attention. Yet, a poor handle design might lead to overexertion and altered movement dynamics, or result in misinterpretation of results in research studies. In this study, twelve healthy subjects performed repetitions of a wrist flexion task against a dynamic load generated by a 1-DOF robotic wrist manipulandum. Three different handle designs were qualitatively and quantitatively evaluated based on wrist movement kinematics and dynamics, patterns of finger and wrist muscle activity, and ergonomics criteria such as perceived comfort and fatigue. The three proposed designs were further compared to a conventional joystick-like handle. Task performance as well as kinematic and kinetic parameters were found to be unaffected by handle design. Nevertheless, differences were found in perceived task difficulty, comfort and levels of muscle activation of wrist and finger muscles, with significantly higher muscle activation when using a joystick-like design, where the handle is completely enclosed by the hand. Comfort was rated high for the flat handle, adapted to the natural curvature of the hand with the fingers extended. These results may inform for the design of handles serving as physical interface in teleoperation applications, robot-assisted rehabilitation and biomechanics/neuroscience research.     

Impact of Internet gambling on problem gambling among adolescents in Italy: Findings from a large-scale nationally representative survey

AimsThe primary aim of the present study was to understand the impact of online gambling on gambling problems in a large-scale nationally representative sample of Italian youth, and to identify and then further examine a subgroup of online gamblers who reported higher rates of gambling problems.DesignData from the ESPAD^®Italia2013 (European School Survey Project on Alcohol and Other Drugs) Study were used for analyses of adolescent Internet gambling.SettingSelf-administered questionnaires were completed by a representative sample of high school students, aged 15–19 years.ParticipantsA total of 14,778 adolescent students.MeasurementsRespondents’ problem gambling severity; gambling behavior (participation in eight different gambling activities, the number of gambling occasions and the number of online gambling occasions, monthly gambling expenditure); Socio-demographics (e.g., family structure and financial status); and control variables were measured individually (i.e., use of the Internet for leisure activities and playing video games).FindingsRates of problem gambling were five times higher among online gamblers than non-online gamblers. In addition, factors that increased the risk of becoming a problem online gambler included living with non-birth parents, having a higher perception of financial family status, being more involved with gambling, and the medium preferences of remote gamblers (e.g., Internet cafes, digital television, and video game console).ConclusionsThe online gambling environment may pose significantly greater risk to vulnerable players. Family characteristics and contextual elements concerning youth Internet gambling (e.g., remote mediums) may play a key role in explaining problem online gambling among adolescents.     

Anticipatory assistance-as-needed control algorithm for a multijoint upper limb robotic orthosis in physical neurorehabilitation

Robotic devices are becoming a popular alternative to the traditional physical therapy as a mean to enhance functional recovery after stroke; they offer more intensive practice opportunities without increasing time spent on supervision by the treating therapist. An ideal behavior for these systems would consist in emulating real therapists by providing anticipated force feedback to the patients in order to encourage and modulate neural plasticity. However, nowadays there are no systems able to work in an anticipatory fashion. For this reason, the authors propose an anticipatory assistance-as-needed control algorithm for a multijoint robotic orthosis to be used in physical ABI neurorehabilitation. This control algorithm, based on a dysfunctional-adapted biomechanical prediction subsystem, is able to avoid patient trajectory deviations by providing them with anticipatory force-feedback. The system has been validated by means of a robotic simulator.Obtained results demonstrate through simulations that the proposed assistance-as-needed control algorithm is able to provide anticipatory actuation to the patients, avoiding trajectory deviations and tending to minimize the degree of actuation. Thus, the main novelty and contribution of this work is the anticipatory nature of the proposed assistance-as-needed control algorithm, that breaks with the current robotic control strategies by not waiting for the trajectory deviations to take place. This new actuation paradigm avoids patient slacking and increases both participation and muscle activity in such a way that neural plasticity is encouraged and modulated to reinforce motor recovery.     

Effect of body-borne equipment on injury of military pilots and aircrew during a simulated helicopter crash

Military helicopter pilots are expected to wear a variety of items of body-borne equipment during flight so as to be prepared for any situation that may arise in combat. Helicopter seats are designed to a specified weight range for an occupant with equipment. This paper investigates how distributing the equipment on the body affects injury potential during a helicopter crash. A finite element model representing a helicopter seat with a fully deformable 50th percentile Hybrid III carrying equipment was developed. The model was subjected to a standard military certification crash test. Various equipment configurations were investigated and analysed to determine its influence on the risk of injury. It was found that placing the equipment low on the torso, i.e. near the thighs, not only reduces the likelihood of injury in the lumbar, spinal region but also provides favourable results in neck and head injury risk when compared to other configurations investigated. In contrast, placing equipment high on the torso, i.e. close to the chin, increases the lumbar load and implicitly, the risk of head injury. A statistical analysis is carried out using the Wilcoxon Signed Rank Test to deliver probability of loads experienced within a certain interval. This study recommends an equipment configuration that improves survivability for an occupant seated on a fixed load energy absorbing seat which is subjected to Military Standard 58095A Test 4.     

Prototyping virtual reality serious games for building earthquake preparedness: The Auckland City Hospital case study

Enhancing evacuee safety is a key factor in reducing the number of injuries and deaths that result from earthquakes. One way this can be achieved is by training occupants. Virtual Reality (VR) and Serious Games (SGs), represent novel techniques that may overcome the limitations of traditional training approaches. VR and SGs have been examined in the fire emergency context; however, their application to earthquake preparedness has not yet been extensively examined.We provide a theoretical discussion of the advantages and limitations of using VR SGs to investigate how building occupants behave during earthquake evacuations and to train building occupants to cope with such emergencies. We explore key design components for developing a VR SG framework: (a) what features constitute an earthquake event; (b) which building types can be selected and represented within the VR environment; (c) how damage to the building can be determined and represented; (d) how non-player characters (NPC) can be designed; and (e) what level of interaction there can be between NPC and the human participants. We illustrate the above by presenting the Auckland City Hospital, New Zealand as a case study, and propose a possible VR SG training tool to enhance earthquake preparedness in public buildings.     

Genetic based discrete particle swarm optimization for Elderly Day Care Center timetabling

The timetabling problem of local Elderly Day Care Centers (EDCCs) is formulated into a weighted maximum constraint satisfaction problem (Max-CSP) in this study. The EDCC timetabling problem is a multi-dimensional assignment problem, where users (elderly) are required to perform activities that require different venues and timeslots, depending on operational constraints. These constraints are categorized into two: hard constraints, which must be fulfilled strictly, and soft constraints, which may be violated but with a penalty. Numerous methods have been successfully applied to the weighted Max-CSP; these methods include exact algorithms based on branch and bound techniques, and approximation methods based on repair heuristics, such as the min-conflict heuristic. This study aims to explore the potential of evolutionary algorithms by proposing a genetic-based discrete particle swarm optimization (GDPSO) to solve the EDCC timetabling problem. The proposed method is compared with the min-conflict random-walk algorithm (MCRW), Tabu search (TS), standard particle swarm optimization (SPSO), and a guided genetic algorithm (GGA). Computational evidence shows that GDPSO significantly outperforms the other algorithms in terms of solution quality and efficiency.     

A methodological approach to the design of optimising control strategies for sewer systems

This study focuses on designing an optimisation based control for sewer system in a methodological way and linking it to a regulatory control. Optimisation based design is found to depend on proper choice of a model, formulation of objective function and tuning of optimisation parameters. Accordingly, two novel optimisation configurations are developed, where the optimisation either acts on the actuators or acts on the regulatory control layer. These two optimisation designs are evaluated on a sub-catchment of the sewer system in Copenhagen, and found to perform better than the existing control; a rule based expert system. On the other hand, compared with a regulatory control technique designed earlier in Mollerup et al. (2015), the optimisation showed similar performance with respect to minimising overflow volume. Hence for operation of small sewer systems, regulatory control strategies can offer promising potential and should be considered along more advanced strategies when identifying novel solutions.     

Continuum electrostatic approach for evaluating positions and interactions of proteins in a bilayer membrane

Orientations of proteins in the membranes are crucial to their function and stability. Unfortunately the exact positions of these proteins in the lipid bilayer are mostly undetermined. Here, the spatial orientation of membrane proteins within the lipid membrane was evaluated using a Poisson–Boltzmann solvent continuum approach to calculate the electrostatic free energy of the protein solvation at various orientations in an implicit bilayer. The solvation energy was obtained by computing the difference in electrostatic energies of the protein in water and in lipid/water environments, treating each as an implicit solvent model. The optimal position of transmembrane proteins (TMP) in a lipid bilayer is identified by the minimum in the “downhill” pathway of the solvation energy landscape. The energy landscape pattern was considerably conserved in various TMP classes. Evaluation of the position of 1060 membrane proteins from the orientations of proteins in membranes (OPM) database revealed that most of the polytopic and β-barrel proteins were in good agreement with those of the OPM database. The study provides a useful scheme for estimating the membrane solvation energy made by lipid-exposed amino acids in membrane proteins. In addition, our results tested with the bacterial potassium channel model demonstrated the potential usefulness of the approach in assessing the quality of membrane protein models. The present approach should be applicable for constructing transmembrane proteins–lipid configuration suitable for membrane protein simulations and will have utility for the structural modeling of membrane proteins.     

Simulating agricultural land rental markets by combining agent-based models with traditional economics concepts: The case of the Argentine Pampas

Land exchange through rental transactions is a central process in agricultural systems. The land tenure regimes emerge from land transactions and structural and land use changes are tied to the dynamics of the land market. We introduce LARMA, a LAnd Rental MArket model embedded within the Pampas Model (PM), an agent-based model of Argentinean agricultural systems. LARMA produces endogenous formation of land rental prices. LARMA relies on traditional economic concepts for LRP formation but addresses some drawbacks of this approach by being integrated into an agent-based model that considers heterogeneous agents interacting with one another. PM-LARMA successfully reproduced the agricultural land tenure regimes and land rental prices observed in the Pampas. Including adaptive, heterogeneous and interacting agents was critical to this success. We conclude that agent-based and traditional economic models can be successfully combined to capture complex emergent land tenure and market price patterns while simplifying the overall model design.     

A Time Flexible Kernel framework for video-based activity recognition

This work deals with the challenging task of activity recognition in unconstrained videos. Standard methods are based on video encoding of low-level features using Fisher Vectors or Bag of Features. However, these approaches model every sequence into a single vector with fixed dimensionality that lacks any long-term temporal information, which may be important for recognition, especially of complex activities. This work proposes a novel framework with two main technical novelties: First, a video encoding method that maintains the temporal structure of sequences and second a Time Flexible Kernel that allows comparison of sequences of different lengths and random alignment. Results on challenging benchmarks and comparison to previous work demonstrate the applicability and value of our framework.